Michael Backes
11 papers in the PaperMetrix corpus
Papers by this author
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From Closed-world Enforcement to Open-world Assessment of Privacy
2015 · arXiv (Cornell University)
In this paper, we develop a user-centric privacy framework for quantitatively assessing the exposure of personal information in open settings. Our formalization addresses key-challenges posed by such open settings, such as the unstructured dissemination of …
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Unleashing Use-Before-Initialization Vulnerabilities in the Linux Kernel Using Targeted Stack Spraying
2017
Author(s): Kangjie Lu, Marie-Therese Walter, David Pfaff, Stefan Nümberger, Wenke Lee, Michael Backes Download: Paper (PDF) Date: 27 Feb 2017 Document Type: Reports Additional Documents: Slides Video Associated Event: NDSS Symposium 2017 Abstract: A common …
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Precise and Scalable Detection of Double-Fetch Bugs in OS Kernels
2018
During system call execution, it is common for operating system kernels to read userspace memory multiple times (multi-reads). A critical bug may exist if the fetched userspace memory is subject to change across these reads, …
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Membership Privacy for Fully Dynamic Group Signatures
2019 · IACR Cryptology ePrint Archive
Group signatures present a compromise between the traditional goals of digital signatures and the need for signer privacy, allowing for the creation of unforgeable signatures in the name of a group which reveal nothing about …
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Towards Plausible Graph Anonymization
2017 · SERVAL (Université de Lausanne)
Social graphs derived from online social interactions contain a wealth of information that is nowadays extensively used by both industry and academia. However, as social graphs contain sensitive information, they need to be properly anonymized …
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Towards Plausible Graph Anonymization
2020
Social graphs derived from online social interactions contain a wealth of information that is nowadays extensively used by both industry and academia. However, as social graphs contain sensitive information, they need to be properly anonymized …
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BAAAN: Backdoor Attacks Against Autoencoder and GAN-Based Machine Learning Models
2020 · arXiv (Cornell University)
The tremendous progress of autoencoders and generative adversarial networks (GANs) has led to their application to multiple critical tasks, such as fraud detection and sanitized data generation. This increasing adoption has fostered the study of …
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Get a Model! Model Hijacking Attack Against Machine Learning Models
2021 · arXiv (Cornell University)
Machine learning (ML) has established itself as a cornerstone for various critical applications ranging from autonomous driving to authentication systems. However, with this increasing adoption rate of machine learning models, multiple attacks have emerged. One …
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Backdoor Attacks Against Dataset Distillation
2023
Dataset distillation has emerged as a prominent technique to improve data efficiency when training machine learning models. It encapsulates the knowledge from a large dataset into a smaller synthetic dataset. A model trained on this …
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In ChatGPT We Trust? Measuring and Characterizing the Reliability of ChatGPT
2023 · arXiv (Cornell University)
The way users acquire information is undergoing a paradigm shift with the advent of ChatGPT. Unlike conventional search engines, ChatGPT retrieves knowledge from the model itself and generates answers for users. ChatGPT's impressive question-answering (QA) …
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FAKEPCD: Fake Point Cloud Detection via Source Attribution
2024
To prevent the mischievous use of synthetic (fake) point clouds produced by generative models, we pioneer the study of detecting point cloud authenticity and attributing them to their sources. We propose an attribution framework FakePCD …